Senior Researcher - Acceleration of AI models

Key Skills

AI technology, C++, Computer Vision (CV), Deep Learning, Effective communication skills, Keras software library, MxNet, Natural Language Processing (NLP), Python Programming, PyTorch, TensorFlow

Job Description

Our team has an immediate permanent opening for a Senior Researcher.

Responsibilities:

• Track the trend of AI theory and technology development in the world and generate research report and proposals for promoting Ascend system accordingly.
• Lead or participate in research of algorithms in accelerating the training of the market-driven AI models (CV/NLP/GNN/…), reaching/exceeding the state of the art accuracy, and develop a proof of concept of the algorithms. Those algorithms include but are not limited to the following: optimizers, loss functions, new model architecture, mix precision, model compression, learning technologies (e.g., meta-learning), etc.
• Publish relevant high-quality AI research papers when necessary and approved, and attend conferences for increasing public awareness of Huawei’s Ascend products; file high-value patents on critical algorithms/processes that are of potential business gain.
• Team with other departments/teams from Huawei’s global research centers for collaboration.
• Assist the team lead on planning of projects and definition of technology/products development road map.

Job requirements

What you’ll bring to the team：

• Master/PhD in Computer Science, Math/Statistics, focusing on AI & Deep Learning with solid publication records.
• 2+ years working experience in optimizing performance of training deep learning models and/or their applications to CV/NLP/GNN domains.
• Solid skills in programming in Tensorflow/Keras/PyTorch/MXNet.
• Hands-on skills in C++/Python programming.
• Excellent documentation skills in writing internal reports and/or publishing research papers.
• Excellent communication skills in internal and external presentation.
• Working knowledge of AI accelerators or the full stack of AI acceleration system is a strong asset.
• Strong math background in optimization (e.g., gradient descending) is a great asset.